论文标题
两足球运动的随机和健壮的MPC：鲁棒性和性能的比较研究
Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance
论文作者
论文摘要
线性模型预测控制（MPC）已成功用于为人形机器人生成可行的行走运动。但是，仅使用强大的MPC（RMPC）方法研究了不确定性对约束满意度的影响，这说明了每次瞬间对有限扰动的最坏情况实现。在这封信中，我们首次提出使用线性随机MPC（SMPC）来解释两足步行中的不确定性。我们表明，SMPC通过容忍违反约束的小（用户定义）概率，为用户（或高级决策者）提供了更大的灵活性。因此，可以调整SMPC以达到任意接近100 \％的约束满意度概率，但没有牺牲基于管子的RMPC的性能。我们将SMPC与RMPC进行了鲁棒性（约束满意度）和性能（最佳）的比较。我们的结果突出了SMPC及其对机器人社区的兴趣的好处，作为处理不确定性的有力数学工具。
Linear Model Predictive Control (MPC) has been successfully used for generating feasible walking motions for humanoid robots. However, the effect of uncertainties on constraints satisfaction has only been studied using Robust MPC (RMPC) approaches, which account for the worst-case realization of bounded disturbances at each time instant. In this letter, we propose for the first time to use linear stochastic MPC (SMPC) to account for uncertainties in bipedal walking. We show that SMPC offers more flexibility to the user (or a high level decision maker) by tolerating small (user-defined) probabilities of constraint violation. Therefore, SMPC can be tuned to achieve a constraint satisfaction probability that is arbitrarily close to 100\%, but without sacrificing performance as much as tube-based RMPC. We compare SMPC against RMPC in terms of robustness (constraint satisfaction) and performance (optimality). Our results highlight the benefits of SMPC and its interest for the robotics community as a powerful mathematical tool for dealing with uncertainties.
